Differences Between Big Data and Data Mining
Big data and data mining concepts have become increasingly integral to healthcare practice globally. Although the terms are often used interchangeably, they have different meanings. Data mining is the process of searching for, acquiring, and processing large amounts of data (Guo & Chen, 2023). The data is then analyzed to uncover insightful patterns and trends for a specific issue.
On the other hand, big data refers to enormous amounts of data covering a large and diverse spectrum. For data sets to be considered big data, they must be in massive amounts and be generated quickly. Both data mining and big data contribute to data analytics that inform decision-making and enhance productivity in the healthcare sector.
Characteristics
Data mining and big data embody the three Vs characteristics: volume, velocity, and variety. Variety is a pivotal aspect as it is the core of enriched information that helps better understand and use the data. Variety in this context refers to the different types of data collected in data mining and the manipulation of these data in an extensive data set.
With modern technology, data can be structured, semi-structured, or unstructured. Structured data comprises well-kept patient records in hospitals and databases, as well as systematically recorded information by global healthcare agencies (Guo & Chen, 2023). Examples of semi-structured data include doctors’ notes, observations, patient feedback, and observations on patient health. Unstructured data, such as searches or reviews, is collected online. This data can be presented in graphs, charts, or written content to help visualize the results.
Application in Healthcare
While big data and data mining individually benefit the healthcare continuum, the variety specifically bolsters how the data is applied. Having a variety of data improves healthcare research aimed at improving public health. For example, data analytics will combine structured and unstructured data to identify inconsistencies. The availability of visual data cultivates understanding that enables the development of comprehensible conclusions (Guo & Chen, 2023).
The variety of big data and data mining techniques enables easier extraction of data from graphs and charts. Using advanced analytical tools, data can be converted into easy-to-interpret charts. This improves the data’s consumability and, thus, its application in decision-making, thereby enhancing healthcare outcomes.
Given this, data mining and big data are essential to addressing the most pressing healthcare needs. Data collected from credible sources is used as evidence of the problem and to inform the performance of the current intervention, while highlighting gaps that need to be addressed. As demonstrated, variety is an additional factor that enriches the value of the data. It enhances understanding and makes it easy to consume and apply the data (Datta & Nwankpa, 2021). As more and more organizations invest in data mining, it is pertinent to understand its central role in merging evidence-based healthcare practice. Data mining and big data should also comply with the required standards to be used accurately to improve healthcare practice.
References
Datta, P., & Nwankpa, J. K. (2021). Digital transformation and the COVID-19 crisis continuity planning. Journal of Information Technology Teaching Cases, 11(2), 81-89.
Guo, C., & Chen, J. (2023). Big data analytics in healthcare. In Knowledge technology and systems: Toward establishing knowledge systems science (pp. 27-70). Singapore: Springer Nature Singapore.
